Optimum designs for next-generation sequencing to discover rare variants for common complex disease

Abstract

Recent advances in next-generation sequencing technologies make it affordable to search for rare and functional variants for common complex diseases systematically. We investigated strategies for enriching rare variants in the samples selected for sequencing so as to optimize the power for their discovery. In particular, we investigated the roles of alternative sources of enrichment in families through computer simulations. We showed that linkage information, extreme phenotype, and nonrandom ascertainment, such as multiply affected families, constitute different sources for enriching rare and functional variants in a sequencing study design. Linkage is well known to have limited power for detecting small genetic effects, and hence not considered to be a powerful tool for discovering variants for common complex diseases. However, those families with some degree of family-specific linkage evidence provide an effective sampling strategy to sub-select the most linkage-informative families for sequencing. Compared with selecting subjects with extreme phenotypes, linkage evidence performs better with larger families, while extreme-phenotype method is more efficient with smaller families. Families with multiple affected siblings were found to provide the largest enrichment of rare variants. Finally, we showed that combined strategies, such as selecting linkage-informative families from multiply affected families, provide much higher enrichment of rare functional variants than either strategy alone.